Fraud detection using machine learning and the effectiveness of different algorithms

Abstract

Fraud has grown to be a significant issue as technology is developed in the banking, insurance, energy, and nearly every other area. current technology like artificial intelligence should be able to identify fraud in the current world. This thesis defines the many types of fraud and their detection methods. At the same time, real-world case analysis was used to evaluate various models and prioritize machine learning preventative techniques. This paper examines the current state of machine learning applications, particularly in the financial sector, and analyzes the critical issue of credit card fraud. It showed the best approach to detect fraud in financial data carried out using the Python programming language in the last chapter, while the theoretical foundations of models were covered in the first two chapters.